On latent-variable model misspecification in structural measurement error models for binary response.
We consider structural measurement error models for a binary response. We show that likelihood-based estimators obtained from fitting structural measurement error models with pooled binary responses can be far more robust to covariate measurement error in the presence of latent-variable model misspecification than the corresponding estimators from individual responses. Furthermore, despite the loss in information, pooling can provide improved parameter estimators in terms of mean-squared error. Based on these and other findings, we create a new diagnostic method to detect latent-variable model misspecification in structural measurement error models with individual binary response. We use simulation and data from the Framingham Heart Study to illustrate our methods.
Huang
Xianzheng
X
Department of Statistics, University of South Carolina, Columbia, South Carolina 29208, USA. huang@stat.sc.edu
Tebbs
Joshua M
JM
D016477:Artifacts; D001699:Biometry / Q000379:methods*; D002986:Clinical Trials as Topic; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015987:Effect Modifier, Epidemiologic; D016015:Logistic Models; D015233:Models, Statistical; D012107:Research Design